Design of Near-Minimum Time Path Planning Algorithm for Autonomous Driving

2013 ◽  
Vol 37 (5) ◽  
pp. 609-617 ◽  
Author(s):  
Dongwook Kim ◽  
Hakgu Kim ◽  
Kyongsu Yi
Keyword(s):  
Path Planning ◽  
Autonomous Driving ◽  
Minimum Time ◽  
Time Path ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Life Extending Minimum-Time Path Planning for a Hexapod Robot

2010 ◽  
Author(s):  
Xin Wu ◽  
Yaoyu Li ◽  
Thomas R. Consi
Keyword(s):  
Fatigue Life ◽  
Path Planning ◽  
Radial Force ◽  
Minimum Time ◽  
Life Extension ◽  
Euler Formula ◽  
Time Path ◽  
Degradation Rates ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This paper presents a life extending minimum-time path planning algorithm for legged robots, with application for a six-legged walking robot (hexapod). The leg joint fatigue life can be extended by reducing the constraint on the dynamic radial force. The dynamic model of the hexapod is built with the Newton Euler Formula. In the normal condition, the minimum-time path planning algorithm is developed through the bisecting-plane (BP) algorithm with the constraints of maximum joint angular velocity and acceleration. According to the fatigue life model for ball bearing, its fatigue life increases while the dynamic radial force on the bearing decreases. The minimum-time path planning algorithm is thus revised by reinforcing the constraint of maximum radial force based on the expectation of life extension. A symmetric hexapod with 18 degree-of-freedom is used for simulation study. As a simplified treatment, the magnitudes of dynamic radial force on proximal joints at the pair of supporting legs are set identical to achieve similar degradation rates on each joint bearing and obtain the dynamic radial force on each joint. The simulation results validate the effectiveness of the proposed idea. This scheme can extend the operating life of robot (joint bearing fatigue life) by modifying the joint path only without affecting the primary task specifications.

scholarly journals Online Local Path Planning on the Campus Environment for Autonomous Driving Considering Road Constraints and Multiple Obstacles

2021 ◽  
Vol 11 (9) ◽  
pp. 3909
Author(s):  
Changhyeon Park ◽  
Seok-Cheol Kee
Keyword(s):  
Path Planning ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Campus Environment ◽  
Vehicle Localization ◽  
Time Operation ◽  
National University ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Multiple Obstacles

In this paper, an urban-based path planning algorithm that considered multiple obstacles and road constraints in a university campus environment with an autonomous micro electric vehicle (micro-EV) is studied. Typical path planning algorithms, such as A*, particle swarm optimization (PSO), and rapidly exploring random tree* (RRT*), take a single arrival point, resulting in a lane departure situation on the high curved roads. Further, these could not consider urban-constraints to set collision-free obstacles. These problems cause dangerous obstacle collisions. Additionally, for drive stability, real-time operation should be guaranteed. Therefore, an urban-based online path planning algorithm, which is robust in terms of a curved-path with multiple obstacles, is proposed. The algorithm is constructed using two methods, A* and an artificial potential field (APF). To validate and evaluate the performance in a campus environment, autonomous driving systems, such as vehicle localization, object recognition, vehicle control, are implemented in the micro-EV. Moreover, to confirm the algorithm stability in the complex campus environment, hazard scenarios that complex obstacles can cause are constructed. These are implemented in the form of a delivery service using an autonomous driving simulator, which mimics the Chungbuk National University (CBNU) campus.

scholarly journals Path Planning for Highly Automated Driving on Embedded GPUs

2018 ◽  
Vol 8 (4) ◽  
pp. 35 ◽  
Author(s):  
Jörg Fickenscher ◽  
Sandra Schmidt ◽  
Frank Hannig ◽  
Mohamed Bouzouraa ◽  
Jürgen Teich
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Autonomous Driving ◽  
Automated Driving ◽  
Computing Power ◽  
Planning Algorithm ◽  
Time Requirements ◽  
Evaluation Platform ◽  
Path Planning Algorithm ◽  
Highly Automated Driving

The sector of autonomous driving gains more and more importance for the car makers. A key enabler of such systems is the planning of the path the vehicle should take, but it can be very computationally burdensome finding a good one. Here, new architectures in ECU are required, such as GPU, because standard processors struggle to provide enough computing power. In this work, we present a novel parallelization of a path planning algorithm. We show how many paths can be reasonably planned under real-time requirements and how they can be rated. As an evaluation platform, an Nvidia Jetson board equipped with a Tegra K1 SoC was used, whose GPU is also employed in the zFAS ECU of the AUDI AG.

Rapid Short-Time Path Planning for Phase Space

2011 ◽  
Vol 23 (2) ◽  
pp. 271-280 ◽  
Author(s):  
Chyon Hae Kim ◽  
◽  
Hiroshi Tsujino ◽  
Shigeki Sugano ◽  
Keyword(s):  
Phase Space ◽  
Path Planning ◽  
Dynamic Models ◽  
Optimal Motion ◽  
Time Path ◽  
Planning Algorithm ◽  
General Phase ◽  
Pruning Algorithms ◽  
Short Time ◽  
Path Planning Algorithm

This paper addresses optimal motion for general machines. Approximation for optimal motion requires a global path planning algorithm that precisely calculates the whole dynamics of a machine in a brief calculation. We propose a path planning algorithm that consists of path searching and pruning algorithms. The pruning algorithmis based on our analysis of state resemblance in general phase space. To confirm precision, calculation cost, optimality and applicability of the proposed algorithm, we conducted several shortest time path planning experiments for the dynamic models of double inverted pendulums. Precision to reach the goal states of the pendulums was better than other algorithms. Calculation cost was 58 times faster at least. We could tune optimality of proposed algorithm via resolution parameters. A positive correlation between optimality and resolutions was confirmed. Applicability was confirmed in a torque based position and velocity feedback control simulation. As a result of this simulation, the double inverted pendulums tracked planned motion under noise while keeping within torque limitations.

A Real-Time Path-Planning Algorithm based on Receding Horizon Techniques

2017 ◽  
Vol 91 (3-4) ◽  
pp. 445-457 ◽  
Author(s):  
M. Murillo ◽  
G. Sánchez ◽  
L. Genzelis ◽  
L. Giovanini
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Receding Horizon ◽  
Time Path ◽  
Planning Algorithm ◽  
Path Planning Algorithm
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